Fetching the paper…
Reading the bibliography…
Diffusion models (DMs) are capable of generating remarkably high-quality samples by iteratively denoising a random vector, a process that corresponds to moving along the probability flow ordinary differential equation (PF ODE).
Reverse-time diffusion equation models
Brian D.O. Anderson · 1982
Earlier work this paper cites.
Estimation of non-normalized statistical models by score matching
Aapo Hyvärinen and Peter Dayan · 2005
Earlier work this paper cites.
Visualizing data using t-sne
Laurens Van der Maaten and Geoffrey Hinton · 2008
Earlier work this paper cites.
A connection between score matching and denoising autoencoders
Pascal Vincent · 2011
Earlier work this paper cites.
Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
Earlier work this paper cites.
Nice: Non-linear independent components estimation
Laurent Dinh, David Krueger, and Yoshua Bengio · 2014
Earlier work this paper cites.
Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Earlier work this paper cites.
The perception-distortion tradeoff
Yochai Blau and Tomer Michaeli · 2018
Earlier work this paper cites.
The unreasonable effectiveness of deep features as a perceptual metric
Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang · 2018
Earlier work this paper cites.
Image2stylegan: How to embed images into the stylegan latent space?
Rameen Abdal, Yipeng Qin, and Peter Wonka · 2019
Earlier work this paper cites.
Rethinking lossy compression: The rate-distortion-perception tradeoff
Yochai Blau and Tomer Michaeli · 2019
Earlier work this paper cites.
Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 2019
Earlier work this paper cites.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
Earlier work this paper cites.
On the variance of the adaptive learning rate and beyond
Liyuan Liu, Haoming Jiang, Pengcheng He, Weizhu Chen, Xiaodong Liu, Jianfeng Gao, and Jiawei Han · 2020
Earlier work this paper cites.
Fourier features let networks learn high frequency functions in low dimensional domains
Matthew Tancik, Pratul Srinivasan, Ben Mildenhall, Sara Fridovich-Keil, Nithin Raghavan, Utkarsh Singhal, Ravi Ramamoorthi, Jonathan Barron, and Ren Ng · 2020
Earlier work this paper cites.
Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
Cited alongside, same era.
Alias-free generative adversarial networks
Tero Karras, Miika Aittala, Samuli Laine, Erik Härkönen, Janne Hellsten, Jaakko Lehtinen, and Timo Aila · 2021
Cited alongside, same era.
Diffwave: A versatile diffusion model for audio synthesis
Zhifeng Kong, Wei Ping, Jiaji Huang, Kexin Zhao, and Bryan Catanzaro · 2021
Cited alongside, same era.
Knowledge distillation in iterative generative models for improved sampling speed
Eric Luhman and Troy Luhman · 2021
Cited alongside, same era.
Encoding in style: a stylegan encoder for image-to-image translation
Elad Richardson, Yuval Alaluf, Or Patashnik, Yotam Nitzan, Yaniv Azar, Stav Shapiro, and Daniel Cohen-Or · 2021
Cited alongside, same era.
Designing an encoder for stylegan image manipulation
I2sb: image-to-image schrödinger bridge
Guan-Horng Liu, Arash Vahdat, De-An Huang, Evangelos A Theodorou, Weili Nie, and Anima Anandkumar · 2023
Later among the works it cites.
Convergence guarantee for consistency models
Junlong Lyu, Zhitang Chen, and Shoubo Feng · 2023
Later among the works it cites.
Null-text inversion for editing real images using guided diffusion models
Ron Mokady, Amir Hertz, Kfir Aberman, Yael Pritch, and Daniel Cohen-Or · 2023
Later among the works it cites.
Consistency models
Yang Song, Prafulla Dhariwal, Mark Chen, and Ilya Sutskever · 2023
Later among the works it cites.
Edict: Exact diffusion inversion via coupled transformations
Bram Wallace, Akash Gokul, and Nikhil Naik · 2023
Later among the works it cites.
Fast sampling of diffusion models via operator learning
Hongkai Zheng, Weili Nie, Arash Vahdat, Kamyar Azizzadenesheli, and Anima Anandkumar · 2023
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Omer Tov, Yuval Alaluf, Yotam Nitzan, Or Patashnik, and Daniel Cohen-Or · 2021
Cited alongside, same era.
Hyperstyle: Stylegan inversion with hypernetworks for real image editing
Yuval Alaluf, Omer Tov, Ron Mokady, Rinon Gal, and Amit Bermano · 2022
Cited alongside, same era.
Elucidating the design space of diffusion-based generative models
Tero Karras, Miika Aittala, Timo Aila, and Samuli Laine · 2022
Cited alongside, same era.
Pivotal tuning for latent-based editing of real images
Daniel Roich, Ron Mokady, Amit H Bermano, and Daniel Cohen-Or · 2022
Cited alongside, same era.
High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
Cited alongside, same era.
Palette: Image-to-image diffusion models
Chitwan Saharia, William Chan, Huiwen Chang, Chris Lee, Jonathan Ho, Tim Salimans, David Fleet, and Mohammad Norouzi · 2022
Cited alongside, same era.
Progressive distillation for fast sampling of diffusion models
Tim Salimans and Jonathan Ho · 2022
Cited alongside, same era.
Later among the works it cites.
Fast ode-based sampling for diffusion models in around 5 steps
Zhenyu Zhou, Defang Chen, Can Wang, and Chun Chen · 2023
Later among the works it cites.
Consistency models made easy, 2024
Zhengyang Geng, Ashwini Pokle, William Luo, Justin Lin, and J. Zico Kolter · 2024
Closest in time.
Analyzing and improving the training dynamics of diffusion models
Tero Karras, Miika Aittala, Jaakko Lehtinen, Janne Hellsten, Timo Aila, and Samuli Laine · 2024
Closest in time.
Consistency trajectory models: Learning probability flow ode trajectory of diffusion
Dongjun Kim, Chieh-Hsin Lai, Wei-Hsiang Liao, Naoki Murata, Yuhta Takida, Toshimitsu Uesaka, Yutong He, Yuki Mitsufuji, and Stefano Ermon · 2024
Closest in time.
Diff-instruct: A universal approach for transferring knowledge from pre-trained diffusion models
Weijian Luo, Tianyang Hu, Shifeng Zhang, Jiacheng Sun, Zhenguo Li, and Zhihua Zhang · 2024
Closest in time.
Video generation models as world simulators
OpenAI · 2024
Closest in time.
Improved techniques for training consistency models
Yang Song and Prafulla Dhariwal · 2024
Closest in time.
Invertible consistency distillation for text-guided image editing in around 7 steps
Nikita Starodubcev, Mikhail Khoroshikh, Artem Babenko, and Dmitry Baranchuk · 2024
Closest in time.
Em distillation for one-step diffusion models
Sirui Xie, Zhisheng Xiao, Diederik P Kingma, Tingbo Hou, Ying Nian Wu, Kevin Patrick Murphy, Tim Salimans, Ben Poole, and Ruiqi Gao · 2024
Closest in time.
Efficient and unbiased sampling of boltzmann distributions via consistency models
Fengzhe Zhang, Jiajun He, Laurence I Midgley, Javier Antorán, and José Miguel Hernández-Lobato · 2024
Closest in time.